The Southwestern Taphonomic Protocol
R. Kyle Bocinsky
Tiffany Clark
Katherine A. Spielmann
July 09, 2018
Introduction
- Understanding taphonomy important for analysis and interpretation of faunal assemblages
- Common “language” of taphonomy essential for inter-site or inter-project comparisons
- Taphonomic processes of course context-specific, but regionally-important processes may be identified
- Here, we apply the Southwestern Taphonomic Protocol, a systematic way to compare taphonomic impacts on faunal assemblages across archaeological sites in the southwestern US
Project Description
This paper was developed out of the NSF-funded project Faunal Resource Depression and Intensification in the North American Southwest: Digital Data and Regional Synthesis. The projects primary objective was to examine whether there was a relationship between the reduction in availability of large game and the intensification of turkey production in the late pre-Hispanic period (1200–1500 CE) in the American Southwest (???). Addressing this topic required access to and the integration of multiple faunal data sets from Ancestral Pueblo sites dating to that time period. During the project these data sets were uploaded into tDAR (the Digital Archaeological Record, tdar.org), providing both the project personnel and the public access to the data. The collection of faunal data sets curated during this project area available as the Archaeological Fauna: US Southwest collection on tDAR. A subset of these data were further refined so as to maximize comparability across data sets and are available as the Southwestern Taphonomic Protocol collection on tDAR; those are the data sets reported on here. Given that these assemblages had been analyzed and recorded by multiple faunal analysts with different coding schemes, integration of the data was facilitated by the development of ontologies within tDAR to which the data sets could be mapped and of new software within tDAR to accomplish the integration (see Kintigh et al. (2018) for details on this component of the project).
Additionally, inter-site comparison of the fauna data also necessitated an examination of the degree to which zooarchaeological remains from these different sites had been affected by taphonomic processes (Bar-Oz and Dayan 2003; Gifford 1981; Lyman 1994; Lyman and Dean 2010). To assess variability in taphonomic history, Tiffany Clark (???) developed a protocol that explores the degree to which different taphonomic factors played a role in assemblage formation. At the end of her portion of the project, we were concerned by the challenge of undertaking the analysis of the diversity of taphonomic variables included in the protocol. Kyle Bocinsky joined the project to help develop this executable paper for the protocol so that it can be routinely used, or modified as necessary, by other researchers interested in the integrated analysis of faunal data. (???) contains details regarding the development of the protocol.
Overview of Paper
The remainder of this paper briefly discusses the development of the taphonomic protocol and executes the protocol on the 32 faunal data sets included as part of our project. This document is an R Markdown document — essentially, it is a text file that includes computer code in the R statistical language that runs all of the analyses discussed here and produces the figures and tables in this document. The HTML or PDF version of this paper (which you are probably reading) is “compiled” from that original text document and related data files, which were developed at https://github.com/bocinsky/swtp and are archived at [ZENODO ARCHIVE URL HERE]. R Markdown allows for dynamic, data-driven analysis and report writing, and fosters reproducible research. As such, we hope that this document will not only serve as an introduction to the Southwestern Taphonomic Protocol, but will also be a template for others’ implementations of (or alterations to) it. Details on using R Markdown can be found at http://rmarkdown.rstudio.com. The executable paper attempts to follow guidelines developed by Ben Marwick (???) for practicing reproducible computing in archaeological research, and was developed using the rrtools package for R (Marwick 2018).
Overview of SWTP
Selection of Variables and Quantification Method
The first step in constructing the taphonomic protocol was to identify the variables that could be used to assess the impact of different natural and cultural processes on assemblage formation. These data could then be used to evaluate the relative degree of taphonomic comparability among assemblages and to identify individual data sets (or components therein) that display substantial taphonomic bias. A review of the literature found that a diversity of attributes has been employed to evaluate taphonomic biases in faunal assemblages (Bar-Oz and Dayan 2003; Bar-Oz and Munro 2004; Behrensmeyer, Allison and Briggs 1991; Lyman 1984, 1985, 1994; Marean 1991; Morlan 1994; Orton 2012; Pickering, Marean and Dominguez-Rodrigo 2003; Stiner 1992, 1994). Although much of this research has focused on examining a single or small set of variables (e.g., density-mediated attrition or fragmentation), several recent studies have sought to provide a more integrated approach to the study of faunal taphonomy (see Bar-Oz and Munro 2004; Marciniak 2001, 2005; and Orton 2012). These analytical schemes incorporate numerous variables in order to evaluate the effects that different taphonomic agents may have had on faunal assemblages.
The relatively comprehensive sets of variables that were employed by Bar-Oz and Munro (2004) provided a basis with which to begin to define the attributes that are most appropriate for the SWTP. However, those authors caution that the influence and combination of taphonomic factors that affect assemblages may differ significantly among time periods and geographic regions, and as such it is important that researchers evaluate the suitability of individual taphonomic variables to address their specific data sets and research questions. Towards this end, (???) identified a subset of the variables evaluated by Bar-Oz and Munro (2004) that appeared to be most applicable to the examination of taphonomic processes in the prehistoric Southwest. The variables that were selected could be evaluated using data that were commonly recorded on Southwestern sites by zooarchaeologists and had the potential not only to inform on natural agents involved in assemblage formation, but also on the degree of influence of various anthropogenic factors.
The variables fell into three broad analytical categories: bone surface modification, fragmentation intensity, and assemblage completeness. In the Southwest, these variables are compared across the three taxonomic categories that are relatively abundant in Southwestern assemblages and are of particular interest to the resource depression analysis completed as part of this project: artiodactyls, lagomorphs, and turkey. Table 1 provides a list of the variables used in the protocol. Analyses assessing these variables have the potential not only to inform on the natural agents (weathering, gnawing, density-mediated attrition, and in situ attrition) that were involved in assemblage formation, but also on the degree of influence of anthropogenic factors involved in processing a carcass. A short description of each variable, including its interpretative potential and means of assessment, is provided below.
{#tab:taphonomic_variables}| Variable | Potential Taphonomic Information |
|---|---|
| Proportion of weathering of artiodactyl bone (NISP) | Damage from natural peri-depositional formation processes |
| Proportion of gnawing on artiodactyl bone (NISP) | Damage from natural peri-depositional formation processes |
| Proportion of burning of artiodactyl bone (NISP) | Damage by human subsistence behaviors |
| Proportion of cut marks on artiodactyl bone (NISP) | Damage by human subsistence behaviors |
| Degree of completeness of artiodactyl bone (NISP) | Fragmentation from both natural and cultural agents |
| Artiodactyl average bone weight (grams) | Fragmentation from both natural and cultural agents |
| Proportion of complete astragals (NISP) | In situ attrition |
| Relationship between bone survivorship (NISP) and bone mineral density | Density-mediated attrition |
| Skeletal part completeness (% NISP by anatomical region) | Human transport and disposal behaviors |
| Bone survivorship (NISP) and food utility index | Human transport behavior |
| Frequency of burning of artiodactyl, lagomorph, and turkey bone (NISP) | Intertaxonomic differences in damage by human subsistence behaviors |
| Frequency of cut marks on artiodactyl, lagomorph, and turkey bone (NISP) | Intertaxonomic differences in damage by human subsistence behaviors |
| Degree of bone completeness for artiodactyls, lagomorphs, and turkeys (NISP) | Intertaxonomic differences in natural and cultural agents |
| Bone survivorship (NISP) and bone mineral density of artiodactyls, lagomorphs, and turkeys | Intertaxonomic differences in natural and cultural agents |
During the development of the protocol (???), although the Number of Identified Specimens (NISP) was used as the primary method of quantification, the Minimum Number of Individuals (MNI) was also calculated for select variables. Analyses of bone survivorship were undertaken using both NISP and MNI counts in order to evaluate the comparability of the results obtained with different quantification methods. Analyses of bone survivorship and bone mineral density produced similar Spearman’s rank correlation coefficient values for the two methods of faunal quantification (Table 2). These results suggest that in most cases, NISP counts can effectively be employed to assess the relationship between bone survivorship and bone mineral density. This conclusion supports the earlier findings by Grayson and Frey (2004), who determined that NISP-based body-part analyses could replicate the results of those obtained using derivative counting methods including MNI, MNE (Minimum Number of Elements), and MAU (Minimal Animal Unit). The NISP requires minimal time to calculate, can be easily replicated, and avoids the problems associated with the use of MNI counts on small-sized assemblages (see discussion in Grayson (2014)). NISP is thus the quantification method used in the execution of the taphonomic protocol in this paper.
The final step in the development of the protocol was the delineation of the quantitative methods that may be used to gain a comparative understanding of how the taphonomic variables patterned across different sites. To this end, it was decided that in most cases, a visual assessment of the quantitative data was the most effective means for assessing taphonomic data across sites and for identifying those assemblages that may have been subject to taphonomic processes significantly different from the others. One of the strengths of this approach is that it allows the researcher to easily identify how, and to what extent, an assemblage may have been taphonomically altered. Based on this information, a researcher can determine whether or not an assemblage displays enough of a taphonomic bias to warrant its exclusion from a particular study. For the analysis of the degree of correlation between element representation and bone mineral density we used Spearman’s rank correlation coefficient, rho. For the rho data, we assessed both the numeric output and the graph. We bootstrap-estimated 95% confidence intervals around Spearman’s rho. In the graphs below, the dot denotes Spearman’s rho, while the line denotes the bootstrapped 95% confidence interval.
To visually compare the distribution of taphonomic data by variable, the proportion of each assemblage affected and the 95 percent confidence interval around the proportion are calculated in this paper. The dot denotes the proportion of each assemblage affected, with the black line indicating the error range at a 95 percent confidence level estimated using an exact binomial test (???). The width of the confidence intervals can be used to assess the accuracy of the estimated proportion and the degree of confidence associated with the estimate. Narrow confidence intervals are often associated with large samples that display a low degree of variance; in contrast, wider confidence intervals tend to reflect more heterogeneous samples or samples that are relatively small in size. The use of the confidence intervals also allows the analyst to determine whether differences among assemblages are the result of taphonomic bias or are more likely attributable to sampling error.
Analysis and Discussion
Data overview
Data reported here are derived from publicly available faunal data sets from single-site contexts (as opposed to large, multi-site project databases). For the purpose of this paper, we chose not to assess taphonomic processes through time or to compare proveniences within sites (though the SWTP can be used for both). Data sets were uploaded to tDAR either by the original faunal analysts or by project team members.
In several cases, including the Mesa Verde (Crow Canyon) and Salinas data sets, a single variable included multiple kinds of taphonomic data (e.g., burning, weathering, and gnawing). In order to include those sites in the taphonomic assessment we decided to disaggregate these data by creating additional variables in a copy of each data set. These copies are also available on tDAR but do not replace the original data sets there. The caveat to the approach taken by the original analysts is that since only a limited number of the several taphonomic conditions could be selected in the original analysis, elements that exhibited more than one condition would have had only one coded. (For example, Crow Canyon’s faunal database includes three modification columns, limiting the number of possible individual codes to three). This means that the results of the calculations undertaken in the executable paper are the minimum proportion of bones that were affected by a taphonomic condition for that site. We recommend future analysis code each type of taphonomic condition separately, preferably using a regionally-appropriate ontology such as the ontologies defined as part of the Archaeological Fauna: US Southwest collection on tDAR.
In a few other cases a single taphonomic variable in our analysis was coded across multiple variables. This was rare, but when it occurred we made a duplicate of the data set in tDAR and created a new synthetic variable that could be mapped to the ontology for that variable. Again, the original data set in tDAR remains intact.
The tDAR integration tool
Once the data sets were uploaded to tDAR, we combined them using tDAR’s integration tool (Kintigh et al. 2018). Integrations use contributor-defined mappings between variable codings in the original data sets and community-defined ontologies to combine data sets with otherwise mutually incompatible categories. The tDAR integration tool is a robust and mature system for combining data sets hosted on tDAR; it allows users to select multiple data sets, choose mutually-mapped ontologies to include in the integration, and export data for further analysis and to share with colleagues.
As part of this project, team members defined 20 ontologies including various taphonomic variables, which we used to integrate the 32 data sets in this analysis. We included ontologies for taxon, element, proximal/distal portion, burning intensity, butchering, completeness, gnawing, and weathering, plus a “count” column for recording NISP and a “display” column for recording weight.
When adding a “count” column (), we selected the mapped variables that corresponded to NISP (as opposed to MNI). In most cases, this meant selecting “Actual #”, “N”, or a variation.
Figure 1: Screen capture of the tDAR integration tool count column selector. Users have to manually select the appropriate “count” variable to represent NISP.
We then added a “display” column for weight(), and selected the appropriate column for every data set in which specimen weight was recorded.
Figure 2: Screen capture of the tDAR integration tool display column selector, showing selected weight variables where available.
Figure 3: Screen capture of the tDAR integration tool integration column selector. For integration columns in our analysis, users should click the “Select values that appear in any column” button in the left hand column.
Figure 4: Screen capture of the tDAR integration tool showing the count, display, and integration columns.
Collapsing taxon data into artiodactyls, lagomorphs, and turkeys
The SWTP focuses on three of the most abundant and easily identifiable taxa in southwestern faunal assemblages: artiodactyls, lagomorphs, and turkeys. However, analysts vary in the level of classification given to specimens within each of these broad classes (). We therefor needed to collapse lower order categories into these higher order ones. We developed a new package for R called treecats that manipulates tree-like categorical data such as the tDAR ontologies and enables users to collapse nested categories in a comprehensive and reproducible way. While the tDAR integration tool allows for collapsing ontologies, it currently depends on the user selecting the appropriate higher-order check boxes, a process that in our experience was too error prone to be sufficiently reproducible.
We collapsed all species in the taxonomic order Artiodactyla, all the species in the taxonomic order Lagomorpha, and included any avian remains categorized as “large aves” in Meleagris gallopavo. Our goal was to conform to estabilished precedent in southwestern archaeology, while using sufficiently broad taxonomic classes so as to account for analyst error.
Thresholds for dataset inclusion
In the initial stages of developing this executable paper we found that there were assemblages in which relevant variables were present, but rarely used, or where sample sizes were far too small to result in meaningful information. Particularly in the case of the rare use of a variable, calculations of proportions affected and confidence intervals resulted in potentially spurious output. We thus established thresholds for inclusion of variables in the taphonomic protocol that would preclude their calculation where results would be misleading. The threshold for inclusion of artiodactyls, lagomorphs, or turkey elements in a calculation was set at an NISP of 25 for each taxonomic category. For the third phalange or astragal to be included there needed to be a minimum of 5 for the element under consideration. For any assemblage to be included in the analysis of a variable, a minimum of 40 percent of the assemblage had to have been coded for that variable.
Related to thresholds, a number of assemblages used the coding option of “indeterminate.” While this meant that the variable was indeed coded for all of the cases, the actual content of that coding did not contribute information to the interpretation of how prevalent the taphonomic condition was in that assemblage. We thus chose to consider those cases uncoded for analyzing taphonomic differences across sites. Therefore sites with more than 60 percent of the cases coded as “indeterminate” fell below the minimum of threshold of 40 percent coded and were not included in the calculation of that variable.
Data overview
This table summarizes the data sets, including the counts of samples with valid measurements across each variable reviewed in the Southwest Taphonomic Protocol.
Dataset and variable inclusion after thresholding (“Table 3”)
Bone surface modification (all taxa)
Burning
- Proportion of assemblage showing evidence of burning
- Burning is often a result of food preparation or refuse disposal practices and as such, can be an important cultural agent in assemblage formation. However, because burning renders bones more susceptible to fragmentation (Stiner et al. 1995), this variable may also provide insight into post-depositional processes. Burning is assessed by calculating the proportion of specimens (NISP) in the artiodactyl, lagomorph, and Meleagris gallopavo assemblages showing signs of exposure to heat (e.g., partial charring, charring, or calcined); these were mapped to “Burned” or “Probably Burned” in the SWUS Fauna Burning Intensity Ontology.
Select a taxon to view its burning data:
Artiodactyla
Lagomorpha
Meleagris gallopavo
Butchering
- Proportion of assemblage showing butchering marks
- The occurrence cut marks in a faunal assemblage may directly inform on butchering practices and processing techniques. The analysis of cut marks is used in the taphonomic study to evaluate the extent of damage resulting from human subsistence behavior. This variable is assessed by calculating the proportion of specimens (NISP) in the artiodactyl, lagomorph, and Meleagris gallopavo assemblages that show evidence of butchering or cut marks; these were mapped to “Butchered” or “Probably Butchered” in the SWUS Fauna Butchering Ontology.
Select a taxon to view its butchering data:
Artiodactyla
Lagomorpha
Meleagris gallopavo
Gnawing
- Proportion of assemblage showing evidence of gnawing
- The extent of rodent and carnivore gnawing in an assemblage may provide direct information regarding peri-depositional formation processes. This variable is assessed by calculating the proportion of specimens (NISP) in the artiodactyl, lagomorph, and Meleagris gallopavo assemblages that were mapped to “Gnawed” in the SWUS Fauna Gnawing Ontology.
Select a taxon to view its gnawing data:
Artiodactyla
Lagomorpha
Meleagris gallopavo
Weathering
- Proportion of assemblage showing evidence of weathering
- The extent of weathering within an assemblage may provide direct information regarding peri-depositional formation processes. Weathering is assessed by calculating the proportion of specimens (NISP) in the artiodactyl, lagomorph, and Meleagris gallopavo assemblages that were mapped to “Weathered” in the SWUS Fauna Weathering Ontology.
Select a taxon to view its weathering data:
Artiodactyla
Lagomorpha
Meleagris gallopavo
Fragmentation intensity
Fragmentation
- Proportion of assemblage that is highly fragmented
- The proportion of highly fragmented bone can be used to evaluate the overall degree of fragmentation among assemblages. As noted by Todd and Rapson (1988: 33–35), a variety of different processes may result in the higher amounts of fragmentation elements within an assemblage. These may include pre-depositional factors related to grease and marrow extraction activities or post-depositional damage resulting from overburden/sedimentary compaction or trampling. The degree of fragmentation of artiodactyl, lagomorph, and Meleagris gallopavo bone was assessed in this study by calculating the proportion of bone within each assemblage that was less than 25 percent complete; these were mapped to “<25%” in the SWUS Fauna Completeness Ontology.
Select a taxon to view its fragmentation data:
Artiodactyla
Lagomorpha
Meleagris gallopavo
Weight
- Bone weight
- There is a broad correlation between fragment size, count, and weight (Lyman 2008: 102–103). Calculating the median bone weight for taxa can provide a means for evaluating the degree of fragmentation associated with a specific assemblage. Here, we show the mean, median, and interquartile range (IQR) of bone weights. In the graph below, the closed circles represent the medians, and the open circles represent the means. Unsurprisingly, the weight distributions are universally right skewed, with low numbers of heavier elements increasing the mean well above the median bone weight.
Artiodactyla
Lagomorpha
Meleagris gallopavo
Assemblage completeness
Astragal completeness
- Proportion of complete artiodactyl astragals
- Marean (1991) has argued that the extent of completeness of certain dense bone elements, such as carpals and tarsals, within an assemblage may be used to indirectly measure the degree of in situ attrition. Specifically, he posits that because these elements have little or no grease or marrow, they should not be subject to bone fragmenting behaviors by humans or carnivores. Rather, a high degree of fragmentation of large mammal carpals and tarsals more likely reflects the post-burial destruction of bones. Marean (1991: 687) further notes that if elements as dense as the astragalus or calcaneus are severely damaged within an assemblage, then it is likely that less dense bones have been completely destroyed. To evaluate in situ attritional processes in the current study, the proportion of complete artiodactyl astragals was calculated for each assemblage using the SWUS Fauna Completeness Ontology as above.
3rd Phalanx completeness
- Proportion of complete artiodactyl 3rd phalanxes
- As stated above, Marean (1991) hypothesized that because dense bones of the carpals and tarsals have little or no grease or marrow, these elements will only rarely be processed by humans for consumption. As such, the degree of fragmentation associated with carpals and tarsals may more directly reflect post-depositional attritional processes. The addition of a second completeness variable—the proportion of completeness for the slightly less dense third phalanx (0.25 bone density value for Odocoileus spp.; Lyman (1994))—allows for more detailed examination of the taphonomic patterns associated with in situ bone attrition. The proportion of complete 3rd phalanxes was calculated for each assemblage using the SWUS Fauna Completeness Ontology as above.
Bone survivorship and mineral density
- Artiodactyl bone survivorship and bone mineral density
- A number of taphonomic studies have shown that bone mineral density may be an important factor in skeletal element representation (Binford 1981; Kreutzer 1992; Lyman 1984, 1994; 1992). This research indicates that certain parts of the skeletons are consistently better preserved in archaeological assemblages due to their high structural density (Lyman 1994: 234–258). To determine if elemental representation within any of the faunal assemblages included in this study had been affected by differential density-mediated attrition, NISP values for 16 different artiodactyl element bone parts—femur (distal/proximal), humerus (distal/proximal), radius (distal/proximal), tibia (distal/proximal), ulna (distal/proximal), pelvis, astragalus, atlas, calcaneus, mandible, and scapula—were compared with bone structural density data (Lyman 1984, 1994). The strength of the relationship between element abundance and bone density was assessed using Spearman’s rank correlation coefficient.
Intertaxonomic comparison
Burning
- Proportion of burning of artiodactyl, lagomorph, and turkey bone
- This variable compares the pattern of burning among subgroups in the assemblage in order to determine if artiodactyl, lagomorph, and turkey remains were differentially affected by food preparation or refuse disposal behaviors. Burning is assessed by calculating the proportion of artiodactyl, lagomorph, and turkey specimens (NISP) in the assemblages that show evidence of exposure to heat (e.g., partial charring, charring, or calcined).
Butchering
- Proportion of butchering marks on artiodactyl, lagomorph, and turkey bone
- This variable compares the proportion of butchering marks among artiodactyl, lagomorph, and turkey remains to examine intertaxonomic variability in the extent of damage resulting from human subsistence behavior. This variable is evaluated by calculating the proportion of artiodactyl, lagomorph, and turkey specimens (NISP) in each artiodactyl that shows evidence of butchering or cut marks.
Fragmentation intensity
- Degree of fragmentation of artiodactyls, lagomorphs, and turkeys
- To assess intertaxonomic variability in fragmentation patterns, the proportion of complete bone within the assemblage was calculated for artiodactyls, lagomorphs, and turkeys. Significant variation in proportion values among these groups may indicate that taphonomic agents differently affected the bones of specific taxon. This variable is determined by calculating the proportion of bone that more than 50 percent complete (NISP) within the artiodactyl, lagomorph, and turkey assemblages.
Bone survivorship
- Bone survivorship and bone mineral density of artiodactyls, lagomorphs, and turkeys
- This variable compares the relationship between bone survivorship and bone mineral density among taxonomic subgroups in order to assess the differential effects of density-mediated attrition on artiodactyl, lagomorph, and turkey remains. To determine if certain subgroups were differentially affected by this bias, NISP values for a number of bone element parts were calculated for artiodactyl, lagomorph, and turkey. The abundance of these elements was then compared with bone structural density data from Odocoileus spp. (Lyman 1984, 1994), leporid (Pavao and Stahl 1999), and Meleagris gallopavo (Dirrigl 2001), respectively. The strength of the relationship between element abundance and bone density for each subgroup was assessed using Spearman’s rank correlation coefficient.
Conclusions
The most useful variables for assessing taphonomy in the SWUS (Kate/Tiffany)
Explore your data before performing an integrated analysis (Kyle)
Only compare datasets with similar types/levels of taphonomic impact, as appropriate (Kate/Tiffany)
Future directions
SWTP R package (the research compendium package for this analysis) (Kyle)
SWTP toolkit (Shiny web tool for comparing a tdar integration to this SWTP analysis) (Kyle)
Widespread adoption of the Southwest Faunal Ontologies (essential for standardizing faunal analyses in the SWUS) (Kate)
References
Bar-Oz, G and Dayan, T 2003 Testing the use of multivariate inter-site taphonomic comparisons: The faunal analysis of Hefzibah in its Epipalaeolithic cultural context, Journal of Archaeological Science, 30(7): 885–900.
Bar-Oz, G and Munro, N D 2004 Beyond cautionary tales: A multivariate taphonomic approach for resolving equifinality in zooarchaeological studies, Journal of Taphonomy, 2(1): 201–221.
Behrensmeyer, A K, Allison, P A and Briggs, D E 1991 Terrestrial vertebrate accumulations. In: Allison, P.A. and Briggs, D.E. (eds.) Taphonomy: Releasing the data locked in the fossil record. London: Springer. pp. 291–335.
Binford, L R 1981 Bones: Ancient men and modern myths. San Diego, California: Academic Press.
Dirrigl, F J 2001 Bone mineral density of wild turkey (meleagris gallopavo) skeletal elements and its effect on differential survivorship, Journal of Archaeological Science, 28(8): 817–832.
Gifford, D P 1981 Taphonomy and paleoecology: A critical review of archaeology’s sister disciplines, Advances in Archaeological Method and Theory, 4: 365–438.
Grayson, D K 2014 Quantitative zooarchaeology: Topics in the analysis of archaelogical faunas. New York: Academic Press.
Grayson, D K and Frey, C J 2004 Measuring skeletal part representation in archaeological faunas, Journal of Taphonomy, 2(1): 27–42.
Kintigh, K W, Spielmann, K A, Brin, A, Candan, K S, Clark, T C and Peeples, M 2018 Data integration in the service of synthetic research, Advances in Archaeological Practice, 6(1): 30–41.
Kreutzer, L A 1992 Bison and deer bone mineral densities: Comparisons and implications for the interpretation of archaeological faunas, Journal of Archaeological Science, 19(3): 271–294.
Lyman, R L 1984 Bone density and differential survivorship of fossil classes, Journal of Anthropological Archaeology, 3(4): 259–299.
Lyman, R L 1985 Bone frequencies: Differential transport, in situ destruction, and the MGUI, Journal of Archaeological Science, 12(3): 221–236.
Lyman, R L 1994 Vertebrate taphonomy. Cambridge, England: Cambridge University Press.
Lyman, R L 2008 Quantitative paleozoology. Cambridge, England: Cambridge University Press.
Lyman, R L and Dean, R M 2010 Prehistoric anthropogenic impacts to local and regional faunas are not ubiquitous. In: Dean, R. (ed.) The archaeology of anthropogenic environments. Center for archaeological investigations occasional papers. Carbondale, Illinois: Southern Illinois University. pp. 204–224.
Lyman, R L, Houghton, L E and Chambers, A L 1992 The effect of structural density on marmot skeletal part representation in archaeological sites, Journal of Archaeological Science, 19(5): 557–573.
Marciniak, A 2001 Scientific and interpretive components in social zooarchaeology: The case of early farming communities in Kujavia, Archaeologia Polona, 39: 87–110.
Marciniak, A 2005 Social changes in the early European Neolithic: A taphonomy perspective. In: O’Connor, T.P. (ed.) Biosphere to lithosphere: New studies in vertebrate taphonomy. Oxford, England: Oxbow. pp. 148–154.
Marean, C W 1991 Measuring the post-depositional destruction of bone in archaeological assemblages, Journal of Archaeological Science, 18(6): 677–694.
Marwick, B 2018 Rrtools: Creates a reproducible research compendium.
Morlan, R E 1994 Bison bone fragmentation and survivorship: A comparative method, Journal of Archaeological Science, 21(6): 797–807.
Orton, D C 2012 Taphonomy and interpretation: An analytical framework for social zooarchaeology, International Journal of Osteoarchaeology, 22(3): 320–337.
Pavao, B and Stahl, P W 1999 Structural density assays of leporid skeletal elements with implications for taphonomic, actualistic and archaeological research, Journal of Archaeological Science, 26(1): 53–66.
Pickering, T R, Marean, C W and Dominguez-Rodrigo, M 2003 Importance of limb bone shaft fragments in zooarchaeology, Journal of Archaeological Science, 30(11): 1469–1482.
Stiner, M C 1992 Overlapping species ‘choice’ by Italian Upper Pleistocene predators, Current Anthropology, 33(4): 433–451.
Stiner, M C 1994 Honor among thieves: A zooarchaeological study of Neandertal ecology. Princeton, New Jersey: Princeton University Press.
Stiner, M C, Kuhn, S L, Weiner, S and Bar-Yosef, O 1995 Differential burning, recrystallization, and fragmentation of archaeological bone, Journal of Archaeological Science, 22(2): 223–237.
Todd, L C and Rapson, D J 1988 Long bone fragmentation and interpretation of faunal assemblages: Approaches to comparative analysis, Journal of Archaeological Science, 15(3): 307–325.
Colophon
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## dplyr 0.7.6 2018-06-29 CRAN (R 3.5.1)
## DT 0.4 2018-01-30 cran (@0.4)
## evaluate 0.10.1 2017-06-24 CRAN (R 3.5.1)
## forcats 0.3.0 2018-02-19 CRAN (R 3.5.1)
## foreign 0.8-70 2017-11-28 CRAN (R 3.5.1)
## ggplot2 3.0.0 2018-07-03 CRAN (R 3.5.1)
## git2r 0.22.0 2018-07-09 Github (ropensci/git2r@032537c)
## glue 1.2.0 2017-10-29 CRAN (R 3.5.1)
## graphics * 3.5.1 2018-07-05 local
## grDevices * 3.5.1 2018-07-05 local
## grid 3.5.1 2018-07-05 local
## gtable 0.2.0 2016-02-26 CRAN (R 3.5.1)
## highr 0.7 2018-06-09 CRAN (R 3.5.1)
## hms 0.4.2 2018-03-10 CRAN (R 3.5.1)
## htmltools 0.3.6 2017-04-28 CRAN (R 3.5.1)
## htmlwidgets 1.2 2018-04-19 CRAN (R 3.5.1)
## httpuv 1.4.4.2 2018-07-02 cran (@1.4.4.2)
## httr 1.3.1 2017-08-20 CRAN (R 3.5.1)
## igraph 1.2.1 2018-03-10 cran (@1.2.1)
## jsonlite 1.5 2017-06-01 CRAN (R 3.5.1)
## knitr 1.20 2018-02-20 CRAN (R 3.5.1)
## labeling 0.3 2014-08-23 CRAN (R 3.5.1)
## later 0.7.3 2018-06-08 cran (@0.7.3)
## lattice 0.20-35 2017-03-25 CRAN (R 3.5.1)
## lazyeval 0.2.1 2017-10-29 CRAN (R 3.5.1)
## magrittr * 1.5 2014-11-22 CRAN (R 3.5.1)
## memoise 1.1.0 2017-04-21 CRAN (R 3.5.1)
## methods * 3.5.1 2018-07-05 local
## mime 0.5 2016-07-07 CRAN (R 3.5.1)
## mnormt 1.5-5 2016-10-15 cran (@1.5-5)
## munsell 0.5.0 2018-06-12 CRAN (R 3.5.1)
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## parallel 3.5.1 2018-07-05 local
## pillar 1.2.3 2018-05-25 CRAN (R 3.5.1)
## pkgconfig 2.0.1 2017-03-21 CRAN (R 3.5.1)
## plotly 4.7.1 2017-07-29 cran (@4.7.1)
## plyr 1.8.4 2016-06-08 CRAN (R 3.5.1)
## promises 1.0.1 2018-04-13 cran (@1.0.1)
## psych 1.8.4 2018-05-06 cran (@1.8.4)
## purrr 0.2.5 2018-05-29 CRAN (R 3.5.1)
## R6 2.2.2 2017-06-17 CRAN (R 3.5.1)
## RColorBrewer 1.1-2 2014-12-07 CRAN (R 3.5.1)
## Rcpp 0.12.17 2018-05-18 CRAN (R 3.5.1)
## readr 1.1.1 2017-05-16 CRAN (R 3.5.1)
## readxl 1.1.0 2018-04-20 CRAN (R 3.5.1)
## remotes 1.1.1 2017-12-20 CRAN (R 3.5.1)
## rlang * 0.2.1 2018-05-30 CRAN (R 3.5.1)
## rmarkdown 1.10 2018-06-11 cran (@1.10)
## rprojroot 1.3-2 2018-01-03 CRAN (R 3.5.1)
## rsconnect 0.8.8 2018-03-09 CRAN (R 3.5.1)
## scales 0.5.0 2017-08-24 CRAN (R 3.5.1)
## shiny 1.1.0 2018-05-17 cran (@1.1.0)
## stats * 3.5.1 2018-07-05 local
## stringi 1.2.3 2018-06-12 CRAN (R 3.5.1)
## stringr 1.3.1 2018-05-10 CRAN (R 3.5.1)
## swtp * 0.0.0.9000 2018-07-09 local (bocinsky/swtp@NA)
## tdar 0.0.0.9000 2018-07-09 Github (bocinsky/tdar@626b05b)
## tibble 1.4.2 2018-01-22 CRAN (R 3.5.1)
## tidyr 0.8.1 2018-05-18 cran (@0.8.1)
## tidyselect 0.2.4 2018-02-26 CRAN (R 3.5.1)
## tools 3.5.1 2018-07-05 local
## treecats 0.0.0.9000 2018-07-09 Github (bocinsky/treecats@2b216a6)
## utils * 3.5.1 2018-07-05 local
## viridisLite 0.3.0 2018-02-01 CRAN (R 3.5.1)
## withr 2.1.2 2018-03-15 CRAN (R 3.5.1)
## xfun 0.3 2018-07-06 cran (@0.3)
## xml2 1.2.0 2018-01-24 cran (@1.2.0)
## xtable 1.8-2 2016-02-05 cran (@1.8-2)
## yaml 2.1.19 2018-05-01 CRAN (R 3.5.1)The current Git commit details are:
## Local: master /Users/bocinsky/Google Drive/Personal Research/swtp
## Remote: master @ origin (https://github.com/bocinsky/swtp)
## Head: [afeebd5] 2018-06-01: updated docs